Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations184
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory93.2 KiB
Average record size in memory518.4 B

Variable types

Text4
Categorical3
Numeric17

Alerts

Type has constant value "Reg"Constant
3:00 PM is highly overall correlated with 3P% and 1 other fieldsHigh correlation
3P% is highly overall correlated with 3:00 PMHigh correlation
3PA is highly overall correlated with 3:00 PMHigh correlation
DR is highly overall correlated with RebHigh correlation
FG% is highly overall correlated with FGM and 1 other fieldsHigh correlation
FGA is highly overall correlated with FGM and 1 other fieldsHigh correlation
FGM is highly overall correlated with FG% and 2 other fieldsHigh correlation
FT% is highly overall correlated with FTA and 1 other fieldsHigh correlation
FTA is highly overall correlated with FT% and 1 other fieldsHigh correlation
FTM is highly overall correlated with FT% and 1 other fieldsHigh correlation
Pts is highly overall correlated with FG% and 2 other fieldsHigh correlation
Reb is highly overall correlated with DRHigh correlation
3:00 PM has 22 (12.0%) zerosZeros
3P% has 22 (12.0%) zerosZeros
FTM has 9 (4.9%) zerosZeros
FTA has 3 (1.6%) zerosZeros
OR has 74 (40.2%) zerosZeros
TO has 9 (4.9%) zerosZeros
Stl has 63 (34.2%) zerosZeros
PF has 39 (21.2%) zerosZeros
+/- has 5 (2.7%) zerosZeros

Reproduction

Analysis started2024-10-14 23:23:17.278361
Analysis finished2024-10-14 23:24:37.597407
Duration1 minute and 20.32 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Date
Text

Distinct132
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Memory size11.4 KiB
2024-10-14T23:24:37.852979image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.6902174
Min length5

Characters and Unicode

Total characters1047
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)46.2%

Sample

1st rowApr 14
2nd rowApr 13
3rd rowApr 10
4th rowApr 6
5th rowApr 4
ValueCountFrequency (%)
dec 40
 
10.9%
jan 38
 
10.3%
nov 28
 
7.6%
mar 26
 
7.1%
feb 24
 
6.5%
apr 14
 
3.8%
oct 14
 
3.8%
3 10
 
2.7%
10 9
 
2.4%
1 9
 
2.4%
Other values (28) 156
42.4%
2024-10-14T23:24:38.470141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
184
17.6%
2 77
 
7.4%
1 71
 
6.8%
e 64
 
6.1%
a 64
 
6.1%
c 54
 
5.2%
r 40
 
3.8%
D 40
 
3.8%
J 38
 
3.6%
n 38
 
3.6%
Other values (18) 377
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1047
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
184
17.6%
2 77
 
7.4%
1 71
 
6.8%
e 64
 
6.1%
a 64
 
6.1%
c 54
 
5.2%
r 40
 
3.8%
D 40
 
3.8%
J 38
 
3.6%
n 38
 
3.6%
Other values (18) 377
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1047
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
184
17.6%
2 77
 
7.4%
1 71
 
6.8%
e 64
 
6.1%
a 64
 
6.1%
c 54
 
5.2%
r 40
 
3.8%
D 40
 
3.8%
J 38
 
3.6%
n 38
 
3.6%
Other values (18) 377
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1047
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
184
17.6%
2 77
 
7.4%
1 71
 
6.8%
e 64
 
6.1%
a 64
 
6.1%
c 54
 
5.2%
r 40
 
3.8%
D 40
 
3.8%
J 38
 
3.6%
n 38
 
3.6%
Other values (18) 377
36.0%

Opp
Text

Distinct60
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Memory size11.0 KiB
2024-10-14T23:24:38.826029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3206522
Min length2

Characters and Unicode

Total characters611
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)3.3%

Sample

1st row@NO
2nd row@MEM
3rd rowGS
4th rowCLE
5th row@WAS
ValueCountFrequency (%)
lac 10
 
5.4%
hou 10
 
5.4%
sac 10
 
5.4%
no 10
 
5.4%
pho 9
 
4.9%
mem 9
 
4.9%
gs 8
 
4.3%
dal 8
 
4.3%
min 7
 
3.8%
den 7
 
3.8%
Other values (20) 96
52.2%
2024-10-14T23:24:39.456343image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
@ 87
14.2%
A 61
 
10.0%
O 56
 
9.2%
C 41
 
6.7%
N 38
 
6.2%
S 37
 
6.1%
L 36
 
5.9%
H 32
 
5.2%
M 31
 
5.1%
E 29
 
4.7%
Other values (11) 163
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 611
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
@ 87
14.2%
A 61
 
10.0%
O 56
 
9.2%
C 41
 
6.7%
N 38
 
6.2%
S 37
 
6.1%
L 36
 
5.9%
H 32
 
5.2%
M 31
 
5.1%
E 29
 
4.7%
Other values (11) 163
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 611
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
@ 87
14.2%
A 61
 
10.0%
O 56
 
9.2%
C 41
 
6.7%
N 38
 
6.2%
S 37
 
6.1%
L 36
 
5.9%
H 32
 
5.2%
M 31
 
5.1%
E 29
 
4.7%
Other values (11) 163
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 611
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
@ 87
14.2%
A 61
 
10.0%
O 56
 
9.2%
C 41
 
6.7%
N 38
 
6.2%
S 37
 
6.1%
L 36
 
5.9%
H 32
 
5.2%
M 31
 
5.1%
E 29
 
4.7%
Other values (11) 163
26.7%

Score
Text

Distinct179
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size12.0 KiB
2024-10-14T23:24:39.960957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.8532609
Min length7

Characters and Unicode

Total characters1629
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique175 ?
Unique (%)95.1%

Sample

1st rowW 124-108
2nd rowW 123-120
3rd rowL 134-120
4th rowW 97-116
5th rowW 125-120
ValueCountFrequency (%)
w 96
26.1%
l 88
23.9%
103-106 3
 
0.8%
121-115 2
 
0.5%
119-115 2
 
0.5%
117-119 2
 
0.5%
145-150 1
 
0.3%
94-101 1
 
0.3%
134-120 1
 
0.3%
97-116 1
 
0.3%
Other values (171) 171
46.5%
2024-10-14T23:24:40.735004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 486
29.8%
184
 
11.3%
- 184
 
11.3%
0 127
 
7.8%
2 116
 
7.1%
W 96
 
5.9%
3 88
 
5.4%
L 88
 
5.4%
9 58
 
3.6%
4 56
 
3.4%
Other values (4) 146
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1629
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 486
29.8%
184
 
11.3%
- 184
 
11.3%
0 127
 
7.8%
2 116
 
7.1%
W 96
 
5.9%
3 88
 
5.4%
L 88
 
5.4%
9 58
 
3.6%
4 56
 
3.4%
Other values (4) 146
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1629
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 486
29.8%
184
 
11.3%
- 184
 
11.3%
0 127
 
7.8%
2 116
 
7.1%
W 96
 
5.9%
3 88
 
5.4%
L 88
 
5.4%
9 58
 
3.6%
4 56
 
3.4%
Other values (4) 146
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1629
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 486
29.8%
184
 
11.3%
- 184
 
11.3%
0 127
 
7.8%
2 116
 
7.1%
W 96
 
5.9%
3 88
 
5.4%
L 88
 
5.4%
9 58
 
3.6%
4 56
 
3.4%
Other values (4) 146
 
9.0%

Type
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
Reg
184 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters552
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReg
2nd rowReg
3rd rowReg
4th rowReg
5th rowReg

Common Values

ValueCountFrequency (%)
Reg 184
100.0%

Length

2024-10-14T23:24:41.052009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T23:24:41.300985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
reg 184
100.0%

Most occurring characters

ValueCountFrequency (%)
R 184
33.3%
e 184
33.3%
g 184
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 552
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 184
33.3%
e 184
33.3%
g 184
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 552
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 184
33.3%
e 184
33.3%
g 184
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 552
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 184
33.3%
e 184
33.3%
g 184
33.3%

Min
Text

Distinct152
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2024-10-14T23:24:41.772349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9184783
Min length5

Characters and Unicode

Total characters1457
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique125 ?
Unique (%)67.9%

Sample

1st row37:41:00
2nd row40:54:00
3rd row35:32:00
4th row35:54:00
5th row36:11:00
ValueCountFrequency (%)
37:24:00 4
 
2.2%
35:18:00 3
 
1.6%
36:47:00 3
 
1.6%
35:56:00 3
 
1.6%
39:05:00 2
 
1.1%
40:07:00 2
 
1.1%
34:41:00 2
 
1.1%
34:00:00 2
 
1.1%
36:16:00 2
 
1.1%
36:32:00 2
 
1.1%
Other values (142) 159
86.4%
2024-10-14T23:24:42.870275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 413
28.3%
: 363
24.9%
3 206
14.1%
4 94
 
6.5%
2 87
 
6.0%
5 70
 
4.8%
1 67
 
4.6%
7 46
 
3.2%
6 43
 
3.0%
9 37
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1457
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 413
28.3%
: 363
24.9%
3 206
14.1%
4 94
 
6.5%
2 87
 
6.0%
5 70
 
4.8%
1 67
 
4.6%
7 46
 
3.2%
6 43
 
3.0%
9 37
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1457
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 413
28.3%
: 363
24.9%
3 206
14.1%
4 94
 
6.5%
2 87
 
6.0%
5 70
 
4.8%
1 67
 
4.6%
7 46
 
3.2%
6 43
 
3.0%
9 37
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1457
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 413
28.3%
: 363
24.9%
3 206
14.1%
4 94
 
6.5%
2 87
 
6.0%
5 70
 
4.8%
1 67
 
4.6%
7 46
 
3.2%
6 43
 
3.0%
9 37
 
2.5%

FGM
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.565217
Minimum3
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:43.225985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q18
median10
Q313
95-th percentile15
Maximum19
Range16
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0543505
Coefficient of variation (CV)0.2890949
Kurtosis-0.40116354
Mean10.565217
Median Absolute Deviation (MAD)2
Skewness0.067844968
Sum1944
Variance9.3290568
MonotonicityNot monotonic
2024-10-14T23:24:43.463431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
13 28
15.2%
10 24
13.0%
12 19
10.3%
9 18
9.8%
8 17
9.2%
7 17
9.2%
11 15
8.2%
14 14
7.6%
6 11
 
6.0%
15 6
 
3.3%
Other values (7) 15
8.2%
ValueCountFrequency (%)
3 1
 
0.5%
4 2
 
1.1%
5 3
 
1.6%
6 11
6.0%
7 17
9.2%
8 17
9.2%
9 18
9.8%
10 24
13.0%
11 15
8.2%
12 19
10.3%
ValueCountFrequency (%)
19 1
 
0.5%
18 2
 
1.1%
17 1
 
0.5%
16 5
 
2.7%
15 6
 
3.3%
14 14
7.6%
13 28
15.2%
12 19
10.3%
11 15
8.2%
10 24
13.0%

FGA
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.271739
Minimum7
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:43.840604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile12
Q118
median20
Q323
95-th percentile27
Maximum31
Range24
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.5858019
Coefficient of variation (CV)0.2262165
Kurtosis-0.18991042
Mean20.271739
Median Absolute Deviation (MAD)3
Skewness-0.20869252
Sum3730
Variance21.029579
MonotonicityNot monotonic
2024-10-14T23:24:44.082558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
19 26
14.1%
23 17
 
9.2%
20 15
 
8.2%
22 15
 
8.2%
21 11
 
6.0%
18 11
 
6.0%
25 10
 
5.4%
17 10
 
5.4%
24 10
 
5.4%
26 9
 
4.9%
Other values (13) 50
27.2%
ValueCountFrequency (%)
7 1
 
0.5%
10 2
 
1.1%
11 4
 
2.2%
12 5
2.7%
13 4
 
2.2%
14 6
3.3%
15 7
3.8%
16 6
3.3%
17 10
5.4%
18 11
6.0%
ValueCountFrequency (%)
31 1
 
0.5%
30 1
 
0.5%
29 5
 
2.7%
28 2
 
1.1%
27 6
 
3.3%
26 9
4.9%
25 10
5.4%
24 10
5.4%
23 17
9.2%
22 15
8.2%

FG%
Real number (ℝ)

HIGH CORRELATION 

Distinct72
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.27663
Minimum25
Maximum83.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:44.354208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile34.935
Q145
median52.6
Q359.1
95-th percentile68.145
Maximum83.3
Range58.3
Interquartile range (IQR)14.1

Descriptive statistics

Standard deviation10.344479
Coefficient of variation (CV)0.19787961
Kurtosis-0.10329984
Mean52.27663
Median Absolute Deviation (MAD)7.25
Skewness-0.032105247
Sum9618.9
Variance107.00825
MonotonicityNot monotonic
2024-10-14T23:24:44.628924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 13
 
7.1%
60 8
 
4.3%
54.5 7
 
3.8%
57.9 6
 
3.3%
56.5 6
 
3.3%
52.6 5
 
2.7%
41.2 5
 
2.7%
65 5
 
2.7%
44.4 4
 
2.2%
66.7 4
 
2.2%
Other values (62) 121
65.8%
ValueCountFrequency (%)
25 1
 
0.5%
27.3 1
 
0.5%
31.6 2
1.1%
32.1 1
 
0.5%
33.3 3
1.6%
34.6 1
 
0.5%
34.8 1
 
0.5%
35.7 1
 
0.5%
36.4 1
 
0.5%
36.8 3
1.6%
ValueCountFrequency (%)
83.3 1
 
0.5%
76.5 1
 
0.5%
75 1
 
0.5%
73.7 2
 
1.1%
72 1
 
0.5%
71.4 1
 
0.5%
68.4 3
1.6%
66.7 4
2.2%
65.2 3
1.6%
65 5
2.7%

3:00 PM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3478261
Minimum0
Maximum9
Zeros22
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:44.882604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile6
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7921186
Coefficient of variation (CV)0.76330979
Kurtosis2.0962407
Mean2.3478261
Median Absolute Deviation (MAD)1
Skewness1.2391082
Sum432
Variance3.2116892
MonotonicityNot monotonic
2024-10-14T23:24:45.113146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 50
27.2%
1 41
22.3%
3 36
19.6%
0 22
12.0%
4 16
 
8.7%
5 8
 
4.3%
6 4
 
2.2%
7 3
 
1.6%
9 2
 
1.1%
8 2
 
1.1%
ValueCountFrequency (%)
0 22
12.0%
1 41
22.3%
2 50
27.2%
3 36
19.6%
4 16
 
8.7%
5 8
 
4.3%
6 4
 
2.2%
7 3
 
1.6%
8 2
 
1.1%
9 2
 
1.1%
ValueCountFrequency (%)
9 2
 
1.1%
8 2
 
1.1%
7 3
 
1.6%
6 4
 
2.2%
5 8
 
4.3%
4 16
 
8.7%
3 36
19.6%
2 50
27.2%
1 41
22.3%
0 22
12.0%

3PA
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:45.311689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.15
Q15
median6
Q38
95-th percentile11.85
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7221014
Coefficient of variation (CV)0.41878483
Kurtosis0.069851464
Mean6.5
Median Absolute Deviation (MAD)2
Skewness0.44452989
Sum1196
Variance7.4098361
MonotonicityNot monotonic
2024-10-14T23:24:45.543335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
5 29
15.8%
6 25
13.6%
7 24
13.0%
4 23
12.5%
8 22
12.0%
9 16
8.7%
3 11
 
6.0%
10 10
 
5.4%
2 6
 
3.3%
12 5
 
2.7%
Other values (4) 13
7.1%
ValueCountFrequency (%)
1 4
 
2.2%
2 6
 
3.3%
3 11
 
6.0%
4 23
12.5%
5 29
15.8%
6 25
13.6%
7 24
13.0%
8 22
12.0%
9 16
8.7%
10 10
 
5.4%
ValueCountFrequency (%)
14 3
 
1.6%
13 2
 
1.1%
12 5
 
2.7%
11 4
 
2.2%
10 10
 
5.4%
9 16
8.7%
8 22
12.0%
7 24
13.0%
6 25
13.6%
5 29
15.8%

3P%
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.186413
Minimum0
Maximum100
Zeros22
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:45.795049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q122.2
median33.3
Q346.625
95-th percentile73.755
Maximum100
Range100
Interquartile range (IQR)24.425

Descriptive statistics

Standard deviation21.832201
Coefficient of variation (CV)0.62047248
Kurtosis0.81556921
Mean35.186413
Median Absolute Deviation (MAD)11.65
Skewness0.54842994
Sum6474.3
Variance476.64501
MonotonicityNot monotonic
2024-10-14T23:24:46.147690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 22
 
12.0%
33.3 19
 
10.3%
25 18
 
9.8%
50 17
 
9.2%
40 16
 
8.7%
42.9 11
 
6.0%
28.6 6
 
3.3%
44.4 6
 
3.3%
37.5 6
 
3.3%
66.7 6
 
3.3%
Other values (22) 57
31.0%
ValueCountFrequency (%)
0 22
12.0%
10 1
 
0.5%
11.1 2
 
1.1%
12.5 3
 
1.6%
14.3 3
 
1.6%
15.4 1
 
0.5%
16.7 6
 
3.3%
20 6
 
3.3%
22.2 3
 
1.6%
25 18
9.8%
ValueCountFrequency (%)
100 5
2.7%
90 1
 
0.5%
75 4
2.2%
66.7 6
3.3%
64.3 1
 
0.5%
60 3
1.6%
58.3 2
 
1.1%
57.1 3
1.6%
55.6 2
 
1.1%
54.5 1
 
0.5%

FTM
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3913043
Minimum0
Maximum12
Zeros9
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:46.547973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum12
Range12
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5965783
Coefficient of variation (CV)0.59130002
Kurtosis-0.063523827
Mean4.3913043
Median Absolute Deviation (MAD)2
Skewness0.48615275
Sum808
Variance6.7422191
MonotonicityNot monotonic
2024-10-14T23:24:46.865917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4 34
18.5%
5 25
13.6%
2 24
13.0%
3 22
12.0%
7 17
9.2%
1 15
8.2%
6 14
7.6%
8 13
 
7.1%
0 9
 
4.9%
9 5
 
2.7%
Other values (3) 6
 
3.3%
ValueCountFrequency (%)
0 9
 
4.9%
1 15
8.2%
2 24
13.0%
3 22
12.0%
4 34
18.5%
5 25
13.6%
6 14
7.6%
7 17
9.2%
8 13
 
7.1%
9 5
 
2.7%
ValueCountFrequency (%)
12 2
 
1.1%
11 2
 
1.1%
10 2
 
1.1%
9 5
 
2.7%
8 13
 
7.1%
7 17
9.2%
6 14
7.6%
5 25
13.6%
4 34
18.5%
3 22
12.0%

FTA
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.798913
Minimum0
Maximum15
Zeros3
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:47.261163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median5
Q38
95-th percentile11
Maximum15
Range15
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0474927
Coefficient of variation (CV)0.52552827
Kurtosis-0.5010095
Mean5.798913
Median Absolute Deviation (MAD)2
Skewness0.31031496
Sum1067
Variance9.2872119
MonotonicityNot monotonic
2024-10-14T23:24:47.679056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
4 26
14.1%
5 25
13.6%
2 20
10.9%
6 18
9.8%
8 17
9.2%
7 16
8.7%
9 14
7.6%
10 13
7.1%
3 12
6.5%
1 8
 
4.3%
Other values (5) 15
8.2%
ValueCountFrequency (%)
0 3
 
1.6%
1 8
 
4.3%
2 20
10.9%
3 12
6.5%
4 26
14.1%
5 25
13.6%
6 18
9.8%
7 16
8.7%
8 17
9.2%
9 14
7.6%
ValueCountFrequency (%)
15 1
 
0.5%
13 1
 
0.5%
12 4
 
2.2%
11 6
 
3.3%
10 13
7.1%
9 14
7.6%
8 17
9.2%
7 16
8.7%
6 18
9.8%
5 25
13.6%

FT%
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Memory size10.9 KiB
100
49 
50
17 
75
16 
66.7
16 
80
10 
Other values (24)
76 

Length

Max length4
Median length3
Mean length2.9456522
Min length1

Characters and Unicode

Total characters542
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)4.3%

Sample

1st row100
2nd row72.7
3rd row80
4th row60
5th row77.8

Common Values

ValueCountFrequency (%)
100 49
26.6%
50 17
 
9.2%
75 16
 
8.7%
66.7 16
 
8.7%
80 10
 
5.4%
71.4 8
 
4.3%
60 7
 
3.8%
87.5 7
 
3.8%
0 6
 
3.3%
77.8 5
 
2.7%
Other values (19) 43
23.4%

Length

2024-10-14T23:24:48.074891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100 49
26.6%
50 17
 
9.2%
75 16
 
8.7%
66.7 16
 
8.7%
80 10
 
5.4%
71.4 8
 
4.3%
60 7
 
3.8%
87.5 7
 
3.8%
0 6
 
3.3%
77.8 5
 
2.7%
Other values (19) 43
23.4%

Most occurring characters

ValueCountFrequency (%)
0 146
26.9%
7 79
14.6%
. 67
12.4%
1 63
11.6%
5 53
 
9.8%
6 45
 
8.3%
8 37
 
6.8%
3 20
 
3.7%
4 14
 
2.6%
9 8
 
1.5%
Other values (2) 10
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 146
26.9%
7 79
14.6%
. 67
12.4%
1 63
11.6%
5 53
 
9.8%
6 45
 
8.3%
8 37
 
6.8%
3 20
 
3.7%
4 14
 
2.6%
9 8
 
1.5%
Other values (2) 10
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 146
26.9%
7 79
14.6%
. 67
12.4%
1 63
11.6%
5 53
 
9.8%
6 45
 
8.3%
8 37
 
6.8%
3 20
 
3.7%
4 14
 
2.6%
9 8
 
1.5%
Other values (2) 10
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 146
26.9%
7 79
14.6%
. 67
12.4%
1 63
11.6%
5 53
 
9.8%
6 45
 
8.3%
8 37
 
6.8%
3 20
 
3.7%
4 14
 
2.6%
9 8
 
1.5%
Other values (2) 10
 
1.8%

OR
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0271739
Minimum0
Maximum6
Zeros74
Zeros (%)40.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:48.415292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1801272
Coefficient of variation (CV)1.1489069
Kurtosis3.4075583
Mean1.0271739
Median Absolute Deviation (MAD)1
Skewness1.6007221
Sum189
Variance1.3927002
MonotonicityNot monotonic
2024-10-14T23:24:48.787440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 74
40.2%
1 61
33.2%
2 32
17.4%
3 10
 
5.4%
4 3
 
1.6%
5 2
 
1.1%
6 2
 
1.1%
ValueCountFrequency (%)
0 74
40.2%
1 61
33.2%
2 32
17.4%
3 10
 
5.4%
4 3
 
1.6%
5 2
 
1.1%
6 2
 
1.1%
ValueCountFrequency (%)
6 2
 
1.1%
5 2
 
1.1%
4 3
 
1.6%
3 10
 
5.4%
2 32
17.4%
1 61
33.2%
0 74
40.2%

DR
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7934783
Minimum0
Maximum19
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:49.138990image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median7
Q38
95-th percentile12
Maximum19
Range19
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.766063
Coefficient of variation (CV)0.40716448
Kurtosis1.8176882
Mean6.7934783
Median Absolute Deviation (MAD)2
Skewness0.71188721
Sum1250
Variance7.6511048
MonotonicityNot monotonic
2024-10-14T23:24:49.454968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
5 33
17.9%
7 31
16.8%
6 26
14.1%
8 24
13.0%
4 14
7.6%
9 12
 
6.5%
10 12
 
6.5%
3 8
 
4.3%
12 6
 
3.3%
11 5
 
2.7%
Other values (7) 13
 
7.1%
ValueCountFrequency (%)
0 1
 
0.5%
1 3
 
1.6%
2 4
 
2.2%
3 8
 
4.3%
4 14
7.6%
5 33
17.9%
6 26
14.1%
7 31
16.8%
8 24
13.0%
9 12
 
6.5%
ValueCountFrequency (%)
19 1
 
0.5%
15 1
 
0.5%
14 1
 
0.5%
13 2
 
1.1%
12 6
 
3.3%
11 5
 
2.7%
10 12
 
6.5%
9 12
 
6.5%
8 24
13.0%
7 31
16.8%

Reb
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.8206522
Minimum0
Maximum20
Zeros1
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:49.696233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.15
Q16
median8
Q39.25
95-th percentile13.85
Maximum20
Range20
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation3.109198
Coefficient of variation (CV)0.39756249
Kurtosis1.2603095
Mean7.8206522
Median Absolute Deviation (MAD)2
Skewness0.54734473
Sum1439
Variance9.6671121
MonotonicityNot monotonic
2024-10-14T23:24:49.933688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
7 31
16.8%
8 28
15.2%
9 22
12.0%
5 19
10.3%
10 16
8.7%
6 15
8.2%
11 13
7.1%
4 13
7.1%
14 4
 
2.2%
3 4
 
2.2%
Other values (9) 19
10.3%
ValueCountFrequency (%)
0 1
 
0.5%
1 3
 
1.6%
2 2
 
1.1%
3 4
 
2.2%
4 13
7.1%
5 19
10.3%
6 15
8.2%
7 31
16.8%
8 28
15.2%
9 22
12.0%
ValueCountFrequency (%)
20 1
 
0.5%
17 1
 
0.5%
16 1
 
0.5%
15 3
 
1.6%
14 4
 
2.2%
13 3
 
1.6%
12 4
 
2.2%
11 13
7.1%
10 16
8.7%
9 22
12.0%

Ast
Real number (ℝ)

Distinct14
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.173913
Minimum2
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:50.171725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median7
Q39
95-th percentile12
Maximum17
Range15
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7385477
Coefficient of variation (CV)0.38173696
Kurtosis0.10504124
Mean7.173913
Median Absolute Deviation (MAD)2
Skewness0.45162682
Sum1320
Variance7.4996436
MonotonicityNot monotonic
2024-10-14T23:24:50.425116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
6 31
16.8%
5 26
14.1%
9 24
13.0%
8 24
13.0%
7 18
9.8%
11 12
 
6.5%
3 12
 
6.5%
4 12
 
6.5%
12 9
 
4.9%
10 8
 
4.3%
Other values (4) 8
 
4.3%
ValueCountFrequency (%)
2 4
 
2.2%
3 12
 
6.5%
4 12
 
6.5%
5 26
14.1%
6 31
16.8%
7 18
9.8%
8 24
13.0%
9 24
13.0%
10 8
 
4.3%
11 12
 
6.5%
ValueCountFrequency (%)
17 1
 
0.5%
14 2
 
1.1%
13 1
 
0.5%
12 9
 
4.9%
11 12
 
6.5%
10 8
 
4.3%
9 24
13.0%
8 24
13.0%
7 18
9.8%
6 31
16.8%

TO
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3695652
Minimum0
Maximum9
Zeros9
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:50.655090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7412852
Coefficient of variation (CV)0.51676851
Kurtosis0.47971552
Mean3.3695652
Median Absolute Deviation (MAD)1
Skewness0.38457392
Sum620
Variance3.0320741
MonotonicityNot monotonic
2024-10-14T23:24:50.850937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 47
25.5%
4 41
22.3%
2 29
15.8%
5 25
13.6%
1 16
 
8.7%
0 9
 
4.9%
6 9
 
4.9%
8 4
 
2.2%
7 3
 
1.6%
9 1
 
0.5%
ValueCountFrequency (%)
0 9
 
4.9%
1 16
 
8.7%
2 29
15.8%
3 47
25.5%
4 41
22.3%
5 25
13.6%
6 9
 
4.9%
7 3
 
1.6%
8 4
 
2.2%
9 1
 
0.5%
ValueCountFrequency (%)
9 1
 
0.5%
8 4
 
2.2%
7 3
 
1.6%
6 9
 
4.9%
5 25
13.6%
4 41
22.3%
3 47
25.5%
2 29
15.8%
1 16
 
8.7%
0 9
 
4.9%

Stl
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1521739
Minimum0
Maximum5
Zeros63
Zeros (%)34.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:51.038526image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1588083
Coefficient of variation (CV)1.0057582
Kurtosis0.98973798
Mean1.1521739
Median Absolute Deviation (MAD)1
Skewness1.0834208
Sum212
Variance1.3428368
MonotonicityNot monotonic
2024-10-14T23:24:51.254431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 64
34.8%
0 63
34.2%
2 34
18.5%
3 15
 
8.2%
4 5
 
2.7%
5 3
 
1.6%
ValueCountFrequency (%)
0 63
34.2%
1 64
34.8%
2 34
18.5%
3 15
 
8.2%
4 5
 
2.7%
5 3
 
1.6%
ValueCountFrequency (%)
5 3
 
1.6%
4 5
 
2.7%
3 15
 
8.2%
2 34
18.5%
1 64
34.8%
0 63
34.2%

Blk
Categorical

Distinct5
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size10.5 KiB
0
93 
1
58 
2
29 
3
 
3
4
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters184
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 93
50.5%
1 58
31.5%
2 29
 
15.8%
3 3
 
1.6%
4 1
 
0.5%

Length

2024-10-14T23:24:51.500538image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-14T23:24:51.759226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 93
50.5%
1 58
31.5%
2 29
 
15.8%
3 3
 
1.6%
4 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 93
50.5%
1 58
31.5%
2 29
 
15.8%
3 3
 
1.6%
4 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 184
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 93
50.5%
1 58
31.5%
2 29
 
15.8%
3 3
 
1.6%
4 1
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 184
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 93
50.5%
1 58
31.5%
2 29
 
15.8%
3 3
 
1.6%
4 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 184
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 93
50.5%
1 58
31.5%
2 29
 
15.8%
3 3
 
1.6%
4 1
 
0.5%

PF
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5652174
Minimum0
Maximum5
Zeros39
Zeros (%)21.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:51.964312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.270122
Coefficient of variation (CV)0.81146681
Kurtosis-0.11397308
Mean1.5652174
Median Absolute Deviation (MAD)1
Skewness0.70153293
Sum288
Variance1.6132098
MonotonicityNot monotonic
2024-10-14T23:24:52.182340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 63
34.2%
2 42
22.8%
0 39
21.2%
3 23
 
12.5%
4 13
 
7.1%
5 4
 
2.2%
ValueCountFrequency (%)
0 39
21.2%
1 63
34.2%
2 42
22.8%
3 23
 
12.5%
4 13
 
7.1%
5 4
 
2.2%
ValueCountFrequency (%)
5 4
 
2.2%
4 13
 
7.1%
3 23
 
12.5%
2 42
22.8%
1 63
34.2%
0 39
21.2%

+/-
Real number (ℝ)

ZEROS 

Distinct56
Distinct (%)30.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.625
Minimum-30
Maximum36
Zeros5
Zeros (%)2.7%
Negative82
Negative (%)44.6%
Memory size1.6 KiB
2024-10-14T23:24:52.448048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-30
5-th percentile-20.85
Q1-7
median2
Q311.25
95-th percentile22
Maximum36
Range66
Interquartile range (IQR)18.25

Descriptive statistics

Standard deviation13.246945
Coefficient of variation (CV)8.1519663
Kurtosis-0.38103302
Mean1.625
Median Absolute Deviation (MAD)9
Skewness-0.098584387
Sum299
Variance175.48156
MonotonicityNot monotonic
2024-10-14T23:24:52.733634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 9
 
4.9%
-6 8
 
4.3%
-5 8
 
4.3%
9 8
 
4.3%
-11 7
 
3.8%
14 7
 
3.8%
7 6
 
3.3%
-4 6
 
3.3%
12 6
 
3.3%
-1 5
 
2.7%
Other values (46) 114
62.0%
ValueCountFrequency (%)
-30 2
1.1%
-28 1
0.5%
-27 1
0.5%
-25 2
1.1%
-24 1
0.5%
-23 1
0.5%
-22 1
0.5%
-21 1
0.5%
-20 2
1.1%
-19 1
0.5%
ValueCountFrequency (%)
36 1
 
0.5%
33 1
 
0.5%
28 1
 
0.5%
26 2
 
1.1%
23 2
 
1.1%
22 5
2.7%
21 1
 
0.5%
20 3
1.6%
19 4
2.2%
18 4
2.2%

Pts
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)20.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.869565
Minimum8
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-10-14T23:24:53.019930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile16
Q123
median27
Q333
95-th percentile40
Maximum56
Range48
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.9571494
Coefficient of variation (CV)0.28551394
Kurtosis0.48971171
Mean27.869565
Median Absolute Deviation (MAD)6
Skewness0.32196923
Sum5128
Variance63.316227
MonotonicityNot monotonic
2024-10-14T23:24:53.281470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
25 16
 
8.7%
26 13
 
7.1%
30 12
 
6.5%
31 10
 
5.4%
33 10
 
5.4%
23 9
 
4.9%
21 9
 
4.9%
28 8
 
4.3%
37 6
 
3.3%
34 6
 
3.3%
Other values (27) 85
46.2%
ValueCountFrequency (%)
8 1
 
0.5%
10 2
 
1.1%
12 1
 
0.5%
13 2
 
1.1%
15 1
 
0.5%
16 5
2.7%
17 3
1.6%
18 6
3.3%
19 6
3.3%
20 6
3.3%
ValueCountFrequency (%)
56 1
 
0.5%
50 1
 
0.5%
48 1
 
0.5%
47 1
 
0.5%
46 1
 
0.5%
43 2
 
1.1%
41 1
 
0.5%
40 3
1.6%
39 4
2.2%
38 5
2.7%

Interactions

2024-10-14T23:24:31.174369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:19.188112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:27.377749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:31.854542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:35.809725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:40.693347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:44.822292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:48.447025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:53.083527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:57.373288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:01.136453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:05.652386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:10.452765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:14.407603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:18.666173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:23.557767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:27.362681image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:31.384929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:19.686996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:27.906232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:32.086303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:36.043555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:41.060874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:45.026147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:48.659230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:53.428069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:57.599315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:01.365443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:05.990119image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:10.699242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:14.636669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:19.033562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:23.809452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:27.603735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:31.595665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:20.020834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:28.281912image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:32.283863image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:36.234704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:41.396647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:45.237648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:48.887225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:53.750917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:57.810921image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:01.566985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:06.287992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:10.939174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:14.889859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:19.298308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:24.003626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:27.824359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:31.816278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:20.385197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:28.670656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:32.481886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:36.432965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:41.588028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:45.453481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:49.096205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:54.110874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:58.042255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:01.797924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:06.595701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:11.154924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:15.117599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:19.597589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:24.243618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:28.037543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:32.013862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:20.687937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:28.988130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:32.687340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:36.631703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:42.020015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:45.637273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:49.307133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:54.458858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:58.245144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:02.346964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:06.890077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:11.356669image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:15.304566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:19.891234image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:24.447263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:28.244992image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:32.296337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:20.995981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:29.206747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:32.934580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:36.885298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:42.253333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:45.845402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:49.544715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:54.703058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:58.469404image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:02.594537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:07.233098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:11.608082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:15.505582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:20.233248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:24.663977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:28.485534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:32.587932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:21.404903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:29.415187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:33.125150image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:37.114084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:42.451880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:46.036032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:49.751130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:54.948005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:58.679103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:02.820226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:07.587685image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:11.833686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:15.701367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:20.560214image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:24.872542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:28.703069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:32.906305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:21.812914image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:29.646754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:33.520120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:37.388400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:42.644974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:46.255906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:49.959087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:55.155648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:58.924663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:03.042279image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:07.935734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:12.060243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:15.951651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:20.869696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:25.091542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:28.961699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:33.256726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:22.198684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:29.875872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:33.768091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:37.697259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:42.882077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:46.495292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:50.454655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:55.368498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:59.159455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:03.297742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:08.317523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:12.290960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:16.201606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:21.214015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:25.329969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:29.190274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:33.555197image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:22.635293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:30.090621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:33.984554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:38.078449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:43.104462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:46.695429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:50.669025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:55.578436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:59.371274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:03.519701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:08.563223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:12.530297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:16.829682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:21.520450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:25.544544image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:29.411597image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:33.879687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:23.000556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:30.318943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:34.217677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:38.445162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:43.332539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:46.925266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:50.896179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:55.832195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:59.587968image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:03.745943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:08.815644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:12.780596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:17.043332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:21.903574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:25.792058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:29.643239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:34.199636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:23.361892image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:30.571487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:34.457774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:38.818557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:43.550956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:47.163086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:51.193213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:56.070442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:59.837819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:04.002676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:09.059810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:13.028378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:17.275594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:22.203818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:26.030898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:29.891810image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:34.517133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:23.979265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:30.809484image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:34.675352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:39.151773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:43.784902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:47.407437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:51.526166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:56.292456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:00.072749image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:04.243562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:09.291222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:13.276035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:17.506134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:22.431636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:26.263190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:30.118674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:34.807283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:24.531543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:31.021609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:34.919154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:39.473946image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:43.984636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:47.602215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:51.827814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:56.518016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:00.270668image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:04.457103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:09.524696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:13.496906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:17.707183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:22.637409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:26.467986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:30.309003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:35.141249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:25.155579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:31.225029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:35.140592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:39.794203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:44.181419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:47.813869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:52.163249image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:56.720585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:00.476756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:04.719963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:09.751618image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:13.746693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:17.942205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:22.895996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:26.671934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:30.523688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:36.036940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:25.725987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:31.434103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:35.364332image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:40.100814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:44.409201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:48.022540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:52.490514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:56.952077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:00.677588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:05.050449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:09.971023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:13.969692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:18.140891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:23.105053image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:26.917177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:30.743381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:36.236071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:26.946980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:31.662809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:35.582318image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:40.347652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:44.619569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:48.233102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:52.833953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:23:57.170585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:00.918304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:05.343229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:10.210757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:14.195838image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:18.370961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:23.353822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:27.146078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-10-14T23:24:30.963662image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-10-14T23:24:53.541282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
+/-3:00 PM3P%3PAAstBlkDRFG%FGAFGMFT%FTAFTMORPFPtsRebStlTO
+/-1.0000.0310.115-0.1290.1440.1520.0090.265-0.0430.1390.000-0.0170.059-0.084-0.0980.124-0.0430.051-0.082
3:00 PM0.0311.0000.7990.642-0.0940.0000.0290.2850.2160.3720.0680.0260.0120.0550.0860.4980.044-0.002-0.028
3P%0.1150.7991.0000.1380.0210.000-0.1110.474-0.0520.2780.111-0.093-0.067-0.022-0.0620.347-0.083-0.041-0.082
3PA-0.1290.6420.1381.000-0.1690.0000.184-0.1810.4850.2590.1140.1290.0550.1230.2140.3620.1670.0430.035
Ast0.144-0.0940.021-0.1691.0000.1200.0460.0110.0060.0010.000-0.011-0.0340.001-0.161-0.0290.0370.0500.033
Blk0.1520.0000.0000.0000.1201.0000.1000.0990.0000.0000.0000.0000.0000.0000.1470.0960.1870.1260.000
DR0.0090.029-0.1110.1840.0460.1001.000-0.0660.1320.0500.0970.2350.1970.1280.0710.0900.905-0.0490.206
FG%0.2650.2850.474-0.1810.0110.099-0.0661.000-0.0520.6090.162-0.0370.005-0.136-0.0620.549-0.088-0.024-0.057
FGA-0.0430.216-0.0520.4850.0060.0000.132-0.0521.0000.7230.1150.1530.1610.2570.1850.6730.1910.092-0.034
FGM0.1390.3720.2780.2590.0010.0000.0500.6090.7231.0000.2570.1040.1470.0910.1240.9270.0800.081-0.048
FT%0.0000.0680.1110.1140.0000.0000.0970.1620.1150.2571.0000.7150.6110.0000.0420.4180.0000.0000.000
FTA-0.0170.026-0.0930.129-0.0110.0000.235-0.0370.1530.1040.7151.0000.9110.0380.0960.3630.2290.0410.075
FTM0.0590.012-0.0670.055-0.0340.0000.1970.0050.1610.1470.6110.9111.0000.0480.0360.4240.1920.0740.088
OR-0.0840.055-0.0220.1230.0010.0000.128-0.1360.2570.0910.0000.0380.0481.0000.0280.0780.4770.047-0.033
PF-0.0980.086-0.0620.214-0.1610.1470.071-0.0620.1850.1240.0420.0960.0360.0281.0000.1310.1180.0790.168
Pts0.1240.4980.3470.362-0.0290.0960.0900.5490.6730.9270.4180.3630.4240.0780.1311.0000.1110.073-0.029
Reb-0.0430.044-0.0830.1670.0370.1870.905-0.0880.1910.0800.0000.2290.1920.4770.1180.1111.000-0.0510.184
Stl0.051-0.002-0.0410.0430.0500.126-0.049-0.0240.0920.0810.0000.0410.0740.0470.0790.073-0.0511.0000.073
TO-0.082-0.028-0.0820.0350.0330.0000.206-0.057-0.034-0.0480.0000.0750.088-0.0330.168-0.0290.1840.0731.000

Missing values

2024-10-14T23:24:36.593436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-14T23:24:37.289457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DateOppScoreTypeMinFGMFGAFG%3:00 PM3PA3P%FTMFTAFT%ORDRRebAstTOStlBlkPF+/-Pts
0Apr 14@NOW 124-108Reg37:41:00112055.0020.06610029111745101928
1Apr 13@MEMW 123-120Reg40:54:00132065.03742.981172.727958201-337
2Apr 10GSL 134-120Reg35:32:00142263.61333.34580167114200-633
3Apr 6CLEW 97-116Reg35:54:00101855.61520.035600551251111024
4Apr 4@WASW 125-120Reg36:11:0091850.0010.07977.825794302925
5Apr 3@TORW 128-111Reg28:47:00101283.311100.02540044910001323
6Apr 1@BKNW 116-104Reg37:12:00131776.591090.05683.3167540002240
7Mar 30@INDL 90-109Reg34:41:0061250.0030.044100191085001-1316
8Mar 28@MEMW 136-124Reg34:45:0081457.1010.071163.6014141241013323
9Mar 25INDW 145-150Reg38:08:0081942.12450.0881001451031111226
DateOppScoreTypeMinFGMFGAFG%3:00 PM3PA3P%FTMFTAFT%ORDRRebAstTOStlBlkPF+/-Pts
174Nov 27SACL 141-137Reg49:45:00102540.021315.481172.7167117104-730
175Nov 25@INDW 124-116Reg43:28:00132944.851241.78988.905562122-339
176Nov 22@DETW 121-116Reg21:264757.11333.31110001152001-1510
177Nov 20@BOSL 108-130Reg32:04:00101662.53742.901015623203-423
178Nov 3HOUW 117-119Reg36:16:00132161.92450.022100044104203830
179Nov 1HOUW 85-95Reg34:32:0061931.6060.0347516782410715
180Oct 30CLEW 101-113Reg37:57:00102245.511010.05510012387311926
181Oct 25MEMW 118-121Reg40:18:0071936.84944.4125006663221-119
182Oct 23PHOL 115-105Reg36:32:0081844.45955.64410002255204-625
183Oct 20GSL 121-114Reg36:44:00132356.551145.536501101154115-234